{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib多图显示\n",
    "首先导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.arange(0,2,0.1)\n",
    "y1,y2,y3,y4 = x, x**2, -x, np.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、Subplot子图\n",
    "\n",
    "### 1. 均匀图中图\n",
    "\n",
    "`plt.subplot(2,2,1)  # 或直接写221`\n",
    "\n",
    "**作用：** 将当前绘图窗口分成2行2列，且当前绘图位置为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cHr9ZTnKUbCg5NYdnlsYRfewc/Vr78cZd7Wlcu7rRsYRwCIfScpnwZTSH03J59tZgxvcN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f597d0da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(x,y1)\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(x,y2)\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(x,y3)\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(x,y4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 不均匀图中图\n",
    "\n",
    "`plt.subplot(2,1,1)` - 先将窗口分为2行1列，在第一行绘图\n",
    "`plt.subplot(2,3,4)` - 再将窗口分为2行3列，在第二行绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SYSyalWt2OaaR4PZD//12JUrBw7OtZpfiU2fCe4wrvD+slvAWn6pr6uDbS7eR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f597cc15c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(2,1,1)  # 也可以直接写211\n",
    "plt.plot(x,y1)\n",
    "plt.subplot(2,3,4)\n",
    "plt.plot(x,y2)\n",
    "plt.subplot(2,3,5)\n",
    "plt.plot(x,y3)\n",
    "plt.subplot(2,3,6)\n",
    "plt.plot(x,y4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、subplots \n",
    "\n",
    "与 `subplot` 类似，不同之处是它会同时返回平分后的多个图像区句柄。可搭配 `plt.tight_layout()`，表示紧凑显示图像。\n",
    "\n",
    "`f, ((ax11, ax12), (ax13, ax14)) = plt.subplots(2, 2, sharex=True, sharey=True)`\n",
    "\n",
    "**参数：**\n",
    "\n",
    "`2,2` - 将窗口分为2行2列\n",
    "\n",
    "`sharex=True` - 共享x坐标轴\n",
    "\n",
    "`sharey=True` - 共享y坐标轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AiFJjFh/a2trqXV1dWXw0EI1du3aNpPVEXdYM6sF810wmAdXV1aWBgYEsPhqIhpnN5xHt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f599b9af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,((ax11,ax12),(ax13,ax14)) = plt.subplots(2,2,sharex=True,sharey=True)\n",
    "ax11.plot(x,y1)\n",
    "ax12.scatter(x,y2)\n",
    "plt.tight_layout()  # 紧凑显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、subplot2grid 分格绘图\n",
    "\n",
    "`subplot2grid()` 函数将窗口划分成网格，然后可在网格中框出任意大小的矩形绘图,如：\n",
    "\n",
    "`axes = subplot2grid((3,3),(1,0),colspan=2,rowspan=2)`\n",
    "\n",
    "**作用：** 将窗口分成 3\\*3 的九宫格，然后以第二行第一个格子作为绘图区 `axes` 左上角，绘图区横向（col）和纵向（row）跨度都为两个格子。接下来可用 `axes.plot()` 绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tMLNlZrbYzCLmktFffK0v3Vs343svLWZnkfqfI4GSs4jUy8bdBxj/1FziY2OY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\nZv4/Zql1p0ivYdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f59830c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "ax1 = plt.subplot2grid((3,3),(0,0),colspan=3)\n",
    "ax1.plot(x,y1)              # 绘制第一幅图\n",
    "ax1.set_title(\"ax1_title\")  # 设置第一幅图的标题\n",
    "ax2 = plt.subplot2grid((3,3),(1,0),colspan=2,rowspan=2)\n",
    "ax2.plot(x,y2)\n",
    "ax3 = plt.subplot2grid((3,3),(1,2),rowspan=2)\n",
    "ax3.plot(x,y3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、gridspec 分格绘图\n",
    "\n",
    "首先导入库 `matplotlib.gridspec`\n",
    "\n",
    "`gs = gridspec.GridSpec(3,3)` 将图像窗口分为 `3*3`，接着 `gs` 可以使用切片操作选取网格范围，并搭配 `plt.subplot` 绘图，例如：\n",
    "\n",
    "`ax1 = plt.subplot(gs[0,:2])` 表示图 `ax1` 占第0行和第0,1列\n",
    "\n",
    "`ax2 = plt.subplot(gs[1, -1])` 表示图 `ax2` 占第1行，最后1列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s1fYSvv3CMjbvOcjt5w7g3osHk6y2jVFJr0oUUpWwxKL6eseM9zdx9R8+4GBV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\niIgkKCUAEZEEpQQgIpKg/j/LW4k9yerWEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f598279320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "# 划分网格\n",
    "plt.figure()\n",
    "gs = gridspec.GridSpec(3,3)\n",
    "# 绘图\n",
    "ax1 = plt.subplot(gs[0,:])\n",
    "ax2 = plt.subplot(gs[1:,1:])\n",
    "ax3 = plt.subplot(gs[1:,0])\n",
    "ax1.plot(x,y1)\n",
    "ax2.plot(x,y2)\n",
    "ax3.plot(x,y3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、图中图 add_axes()\n",
    "\n",
    "### 法一：\n",
    "\n",
    "`ax1 = fig.add_axes([left,bottom,width,height])`\n",
    "\n",
    "**作用：** 在图像窗口 `fig` 中添加一个 `axes`，其中 `left,bottom` 为左下角坐标，取值范围是 `0~1` ，`width,height` 是这张图的长宽。\n",
    "\n",
    "### 法二：\n",
    "\n",
    "`plt.axes([left,bottom,width,height])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lljz99NNl7nPkyBHs/X7bt2+nuLiYunXrmhLPuXPnyM3NLX1/zZo1tGnT5pJ9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f597e1fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建数据\n",
    "X = [1, 2, 3, 4, 5, 6, 7]\n",
    "Y = [1, 3, 4, 2, 5, 8, 6]\n",
    "# 绘图\n",
    "fig = plt.figure()\n",
    "# 法一\n",
    "ax1 = fig.add_axes([0.1,0.1,0.9,0.9])\n",
    "ax1.plot(X, Y, 'r')\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('y')\n",
    "ax1.set_title('title')\n",
    "\n",
    "ax2 = fig.add_axes([0.2, 0.6, 0.25, 0.25])\n",
    "ax2.plot(Y, X, 'b')\n",
    "ax2.set_xlabel('x')\n",
    "ax2.set_ylabel('y')\n",
    "ax2.set_title('title inside 1')\n",
    "# 法二\n",
    "plt.axes([0.6, 0.2, 0.25, 0.25])\n",
    "plt.plot(Y[::-1], X, 'g') # 注意对y进行了逆序处理\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('title inside 2')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 六、次坐标轴 axes.twinx\n",
    "\n",
    "有时候我们会用到次坐标轴，即在同个图上有第2个y轴存在。\n",
    "\n",
    "`ax2 = ax1.twinx()`\n",
    "\n",
    "**作用：**得到 `ax1` 的镜像坐标系 `ax2`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QQAbLS8Be4L+4ESRdgeTmlDLmH3LmhPffd4Mz3n4bDh6ECRPc9sxu+c7lhMeE\n8+NfP3JvlXt5q8VbXJbvMq/LMsYTqrhMEdKUKf4MxGivg7RqgudjZLCsB573v1yTlWXLBm+9BUWL\nwnPPwd9/u0l38+b1urLAOH7mOM98/QwjV46kVKFSzLlnDq2utek6jfFpr8o/M0XwO1P8Ca3jMli6\nAh/jmnZdsPW0TCqJwLPPuhZX375udOHMmVC4sNeVpa8F2xcQMTOCHYd20Pemvrza5FUK5i7odVnG\nBJPjIlx0pvhzdeEeoBNuMMU+4C7fNmNSrXdvmDIFVqyAhg0zz3yFh04dInxGOM3+24yc2XKyuPti\n3m39rgWWMf+WpkwJ2OhBL9hAjNCRmeYr/OKnL+gzuw/7j+9nQN0BPN/gefLmzKR9nybTCbXRg1lo\nHJcJJufmKzxwwM1XuCnJtQCC275j++j0WSdu++Q2Ls9/Od9HfM+rTV+1wDImgCy0jGfOzVcILrjm\nz/e2Hn+pKh+s/4AKoyow4+cZvNz4ZVZFrKJGiRopn2yMSRMLLeOpSpVg+XK46ipo1QrGj/e6ogv7\n/fDvtJ7cmm5fdKNCsQqs77Wep+s9Tc7sWWAMvzFB4KJCSwZLstPOG5NaZcq4+QqbNYPISBgwAOLj\nva7qn+I1nlHfj6Li6Ios+d8SRrYcyZL7l3BD0Ru8Ls2YkCHCDSI08c09mHB7S39f42JbWoMv8jxj\nklSokBsC36cPDBsGd94JJ054XZXz818/02BSA/rN7cfNpW5mU59NPFj7QbKJdVQY4y8RHgJm4Bb/\n3SRChwS7X/H3dZK9T0sGy4bkdgFX+PsGxvgrRw5491247jp45BFo0ABiYqCER/OvnI07y/Dlw3nh\nmxfImzMv73V4j25Vu9kUTMZcnAjgRlWOiVAW+FyEsqqMIIX1uhK60M3FVwAtgL8TbRdgWSqLNcYv\nItC/P5QvD126QO3absLdKlUyto4f9v5AeEw4P/zxA7dXuJ1RrUdRvEDxjC3CmMwlmyrHAFTZIUJD\nXHBdRTqF1iyggA7SdYl3yGD5JnW1GpM67drBkiVuTa5bb3XTPrXKgJmQTsWe4qVvX+L1pa9TNF9R\nPr/rc+4IuyPwb2xM5rdPhGqqrAPwtbjaAhOByv6+yIU65a8kmcUXdZDajBgm4KpXh++/h2uuceE1\nenRg32/p70upFlWNV757hXur3suWvlsssIwBRGSiiOwXkSTvqBSRhiJyWETW+R5JzSMYD+RJuEGV\nWFXuA+orhyZyAAAeq0lEQVT7W8uFQmsiME8GyzMyWGw8r/FEyZLuXq42bdychY88AnFx6fsex84c\n46G5D1HvvXqcij3FvP/M470O73Fp3kvT942MCV2TIMURfktUtZrv8WIS+8cCH4jwjAj/yBRVlvpb\nyAWncZLBUgB4zlfsf3FJ6d5kkL7p75tkFJvGKfOKi4PHH3fLm7RrB5MnQ4ECKZ+Xkq9+/YrImZH8\nfvh3+tXqxytNXqFArnR4YWNChL/TOIlIWWCWqlZKYl9D4HFVbXvh1yD5TFH8ypSUxuyewc2+mxso\nmOhhTIbJnt0tbzJqFMyeDfXrw+4kO6/9c/DkQe6fcT8tPmxBnhx5WHL/Eka2GmmBZczFqysiG0Rk\nrohUTOaYNGfKhYa8twTeBGKAGjpIU33XjIi0BEYA2YEJqvpaov0DcAuAnaulAlBMVQ+KyA7gKBAH\nxKpqzdS+v8l8+vSBcuWgUyeoVcuNLKxePXWvMXXLVPrO6ctfJ/7i6Vuf5rkGz5EnR56UTzQmc8oh\nIqsTPB+nquNS+RprgTKqekxEWgNfANcmPMB3A/H5TFEu6k7MZLsHZbAsAXrpIN18US8skh3YCjQD\ndgGrgC6quiWZ49sBj6hqY9/zHUBNVf3L3/e07sGsY8MGNzjj4EG31Em7dimfs/foXvrN7ce0H6dR\nvXh1JnaYSLXi1QJfrDFBLD26B5M4dgeJfn+L4DJFuahMOSfZ7kEdpPUuNrB8agHbVHW7qp7BLfjV\n4QLHdwGmpOH9TBZSpQqsXAkVKkDHjvDmm5Dc5VlVZdK6SYSNDmP21tm81uQ1VvZcaYFlTDoRkeLi\nu+teRGrhsuVAwmNUqZfWwAL/Vi6+WCWBnQme7wJqJ3WgiOTDXZjrl2CzAgtEJA4YexHNVZPJlSgB\n33wD3brBY4/BqlUwYQLkT/A3445DO4icGcn87fO5tcytTGg3geuLXu9ZzcaEIhGZAjQEiorILmAQ\nuBGAqhoF3An0FpFY4CTQWQO0WGMgQys12gFLVfVggm23qupuEbkcmC8iP6nq4sQnikgkEAmQK1eu\njKnWBI38+eGzz+D11+GZZ9y6XNOmwdXXuAlun1r4FCLCqNaj6FWzl80XaMxFUNUuKex/F3g3I2oJ\nZGjtBkoneF6KZG5WBjqTqGtQVXf7/t0vItNx3Y3/Ci1fC2wcuGtaaS/bhBoRePJJuPFGN/VTjRvj\nKNX9KX66bCgtr2lJVJsoripylddlGmPSQSD/7FwFXCsi5UQkFy6YYhIfJCKFgQa42X/PbcsvIgXP\nfQ00B0JwbVuTkRo2Pkv30e9wvOA6fnrnDdrvXs/Mu+dYYBmTiQSspaWqsSLSD5iHG/I+UVU3i0gv\n3/4o36G3AV+pasJhf1cA033X9XIAk1X1y0DVakLf2r1r6TGjB+v3reeOoV3JOXc8H4+vQrud8NFH\ncKlNbmFMpnDBGTFCjQ15z3pOnj3J4G8HM2zZMIrlL8aYNmPoeENHVGHcOHjwQShVyl3nqmaDBY35\nF3+HvAcLCy0Tspb8bwk9Z/Zk64Gt9KjWg2HNh3FJ3kv+cczKlXDHHXDggAuxe+/1qFhjglSohZYN\npTIh5+jpo/Sd3Zf6k+pzJu4M8++dT3SH6H8FFrj1uNasgTp14L77oF8/OHPGg6KNMenCWlompMz9\nZS4PzHqAXUd20b92f4Y0HkL+XCn/kRgb60YYDh8Odeu6YfJXXpkBBRsT5KylZUwAHDhxgPum30fr\nya0pmLsgy8KX8VbLt/wKLIAcOWDYMPj4Y1i/3g2PX7IkwEUbY9KdhZYJaqrKp5s/pcKoCkzZNIXn\n6j/H2si11ClV56Je7+673XWuggWhcWMYMSL56Z+MMcHHugdN0NpzdA995/Tli5++4MYSNzKxw0Sq\nXFElXV778GE3/dOMGXDPPW6QRv6Q6SAxJv1Y96AxaaSqRK+NJmxUGF9u+5I3mr7Bip4r0i2wAAoX\ndsPghwxxs8TXquVmjjfGBDcLLRNUtv+9nab/bUrPmT2pWrwqG3ptYMAtA8iRLf3vg8+Wzc1XOG+e\nW+KkVi0YOdK6C40JZhZaJijExcfx9oq3qTymMqt2r2JMmzEs6raIay+7NuWT06hZM9fKatYM+vd3\n63Tt3x/wtzXGXAS7pmU8t3n/ZsJjwlm5eyWtr21NVJsoShcunfKJ6UwVRo92y5wULgzvvw8tW2Z4\nGcZkKLumZYyfzsSd4aVvX6L62OpsO7iNj27/iFldZnkSWOBmi+/bF1avhssvh1at4JFH4PRpT8ox\nxiTBWlrGE6t2ryI8JpyN+zfSuVJnRrYcSbH8xbwu6/+dPAkDB8I770DVqjB5MoSFeV2VMenPWlrG\nXMCJsycY8NUA6kTX4cDJA8zoPIMpd0wJqsACyJvXDcqYNQv27HE3I0dF2SANY7xmLS2TYb7Z8Q0R\nMyPYdnAbETUiGNpsKIXzFPa6rBT98Ye7p+urr6BDB5gwAYoW9boqY9KHtbSMSeTwqcP0mtWLRu83\nIl7jWXjfQsa1GxcSgQVQvDjMnQtvvun+rVoVFi70uipjMo6ITBSR/SKS5GK84owUkW0iskFEagSq\nFgstE1Czt86m4uiKjF87nkfrPMrG3htpXK6x12WlWrZsblDGypVQqJAbHj9woM0Yb7KMScCFxtK2\nAq71PSKBMYEqxELLBMSfx/+k67SutJ3SliJ5irCsxzKGtxhOvpz5vC4tTapVc0udREbCG2+4GeO3\nbvW6KmMCS1UXAwcvcEgH4AN1VgBFRKREIGoJaGiJSEsR+dnXZHwyif0NReSwiKzzPZ7391wTnFSV\njzd9TNjoMD7b/BmDGgxi7QNrqV2qttelpZt8+dygjGnT4LffoEYNGD/eBmmYLK0ksDPB812+beku\nYKElItmBUbhmYxjQRUSSGjS8RFWr+R4vpvJcE0R2H9lNh4870GVqF8oVKceayDW80PAFcmXP5XVp\nAXHbbW4mjdq1XcuraVPYvt3rqoxJtRwisjrBI9Lrgi4kkC2tWsA2Vd2uqmeAj3FNyECfazKYqjJ+\nzXjCRoexYPsChjcfzvLw5VS+orLXpQVcyZIwfz6MHQurVkHlyvD22xAX53VlxvgtVlVrJniMu4jX\n2A0knBWglG9bugtkaPnbXKzrG20yV0QqpvJc47FfD/5Kkw+aEDkrkhtL3MjG3ht59OZHyZ4tu9el\nZZhs2VxLa8sWaNTIDdi45RbYvNnryozJMDHAfb5RhHWAw6q6NxBv5PVAjLVAGVWtArwDfJHaFxCR\nyHPN2tjY2HQv0CQtLj6O4cuGU3lMZdbsXcPYtmNZeN9Crr70aq9L80ypUjBzpps949dfoXp1ePFF\nG2FoQp+ITAGWA9eLyC4RCReRXiLSy3fIHGA7sA0YD/QJWC2BurlYRG4GXlDVFr7nTwGo6qsXOGcH\nUBM3bDJV54LdXJxRNu3fRI8ZPVi1ZxXtrmvHmDZjKFnIGsIJ/fmnmzF+yhTXZRgdDTfd5HVVxvyb\n3Vx83irgWhEpJyK5gM64JuT/E5HiIiK+r2v56jngz7km452JO8ML37xAjbE12HFoBx/f8TEzOs+w\nwEpCsWKuxRUT49bqqlMHBgyAEye8rsyY0Baw0FLVWKAfMA/4EfhUVTcnalLeCWwSkfXASKCzb5x/\nkucGqlaTsu93f0+NsTUY/O1gOlXsxJa+W7i70t34/uYwyWjXzl3b6tkThg1zs2l8843XVRkTumzu\nQXNBx88c5/lFz/P2yre5suCVRLWJos11bbwuKyQtWgQREe561wMPwOuvu3W7jPFSqHUPWmiZZH39\n29dEzIxg+9/b6V2zN681fY1CuQt5XVZIO3ECnn8e3noLSpRwNym3bet1VSYrC7XQ8nr0oAlCh04d\nIiImgiYfNCGbZOObbt8wus1oC6x0kC+f6yZcvhwuucR1H95zjxu4YYxJmYWW+YeYn2OoOLoiE9dN\n5Im6T7Ch1wYalG3gdVmZTq1abg7DF16Azz+H6693C07aXRvGXJiFlgFg//H9dP68Mx0+7kDRfEVZ\n2XMlrzd7nbw583pdWqaVKxcMGgQ//ODmL3zoITchry17YkzyLLSyOFXlow0fETYqjOk/TeelRi+x\nOmI1Na+s6XVpWUbFim4qqOnT3TWvpk3h9tttHkNjkmIDMbKwnYd30mt2L+b8Moc6peoQ3T6asGI2\nL7GXTp1yi02+8orrKnzsMXjqKShQwOvKTGYVagMxLLSyoHiNZ+zqsQxcMJA4jeOVxq/Qr1a/LDVf\nYLDbvRuefBI+/BCuvNKt3XXPPWC3xZn0ZqHlIQutlG09sJWImREs/t9impZvyri24yh3STmvyzLJ\nWLbMTQe1ejXcfDOMHAk1refWpKNQCy27ppVFxMbH8sbSN6gaVZUN+zYwsf1EvvrPVxZYQa5uXVi5\nEiZOdNe4atWC8HDYt8/ryozxhrW0soD1f6ynR0wP1u5dy2033Mao1qMoUTAgK2GbADpyBF56CUaM\ngDx53E3KDz3kRiEac7GspWWCxunY0zz39XPUHF+TXUd28dldnzG101QLrBBVqBAMHQqbNkH9+m4C\n3sqVYc4cryszJuNYaGVSy3Yuo/rY6gxZMoR7Kt/Dlj5buDPsTpvgNhO47jqYNet8WLVpAy1auJWT\njcnsLLQymWNnjtF/bn9unXgrx88eZ27Xubzf8X0uy3eZ16WZdNaqFWzcCMOHu9k1atWCDh1g/Xqv\nKzMmcOyaViYy/9f5RM6KZMehHfS9qS+vNnmVgrkLel2WyQBHj7qRhcOGwaFDcOedMHgwhNltdyYF\ndk3LZLi/T/5Njxk9aP5hc3Jlz8Xi7ot5t/W7FlhZSMGC8Mwz8Ntv8Nxz8OWXUKkS/Oc/8MsvXldn\nTPqxllaIm/7jdPrM6cOfx//kiVue4PkGz5MnRx6vyzIe++svN2jj3Xfh9Gm47z432rBsWa8rM8HG\nWlomQ/xx7A/u+uwubv/0dooXKM73Ed/zSpNXLLAMAEWLukUmt2+HBx+EyZPdAI7evWHXLq+rM6FG\nRFqKyM8isk1Enkxif0MROSwi63yP5wNWSyBbWiLSEhgBZAcmqOprifZ3BQYCAhwFeqvqet++Hb5t\ncUCsqqY4D0BWaGmpKv/d8F8e/vJhTpw9waAGg3i87uPkzJ7T69JMENu9G15+GSZMgGzZoFcvN01U\n8eJeV2a8llJLS0SyA1uBZsAuYBXQRVW3JDimIfC4qgZ8SdOAtbR83+gooBUQBnQRkcSXhX8DGqhq\nZeAlYFyi/Y1UtZo/gZUV/O/Q/2j1USu6fdGNCsUqsK7XOp6q95QFlklRyZIwejRs3equc737LpQv\nD0884boSjbmAWsA2Vd2uqmeAj4EOXhUTyO7BFL9RVV2mqn/7nq4ASgWwnpAVr/GM+n4UlcZU4rvf\nv+OdVu+w5P4l3FD0Bq9LMyGmbFnX2vrpJzfCcNgwKFfOzSS/Z4/X1ZkgVRLYmeD5Lt+2xOqKyAYR\nmSsiFQNVTCBDy99v9JxwYG6C5wosEJE1IhIZgPpCws9//UyDSQ3oN7cfdUvXZVOfTfSr1Y9sYpcj\nzcW75hr44APYvNndnPzGGy7Q7r0X1q71ujqTwXKIyOoEj4v5fbsWKKOqVYB3gC/St8TzguI3n4g0\nwoXWwASbb1XVarjuxb4iUj+ZcyPP/bBjM9Fa5WfjzvLad69RNaoqm/dvZlKHSXzZ9UvKFinrdWkm\nE6lQAT7+2A2L79MHvvgCbrwRGjWCmTMhPt7rCk0GiFXVmgkeiS/T7AZKJ3heyrft/6nqEVU95vt6\nDpBTRIoGothAhlaK3yiAiFQBJgAdVPXAue2qutv3735gOq678V9Uddy5H3aOHDnSsXzv/LD3B2pP\nqM1TC5+i3fXt2NJ3C92qdbMpmEzAlC8Pb7/tRhYOGwa//grt28MNN7hrYZl8fJO5sFXAtSJSTkRy\nAZ2BmIQHiEhx8f2CEpFauGw58K9XSgeBDC1/vtEywDTgXlXdmmB7fhEpeO5roDmwKYC1BoVTsad4\nZuEz3DT+JvYc3cPUTlP57K7PKF7AhniZjFG4sFsteft21wK75BLo2xdKl3bXvXb/689Ok9mpaizQ\nD5gH/Ah8qqqbRaSXiPTyHXYnsElE1gMjgc4aoKHpgR7y3hp4GzfkfaKqvnzum1TVKBGZANwB/M93\nSqyq1hSR8rjWFUAOYLKqvpzS+4XykPelvy8lPCacnw/8zP3V7md48+FckvcSr8syWZwqLF8Ob74J\n06e74fKdO8Mjj0CNGl5XZ9JDqN1cbDNieOzo6aM8vfBpRq0aRZnCZRjXbhzNr27udVnG/Mv27W5+\nw+hoOHYMGjSARx+Ftm1dmJnQZKHloVALrXnb5hE5K5Kdh3fyYK0HebnJyxTIVcDrsoy5oMOH3bD5\nESNg5043ErFfP+ja1c3EYUKLhZaHQiW0Dp48yKPzHuX99e9zQ9EbmNBuAreUucXrsoxJldhYmDoV\n3noLVq6EnDmhXTvo3h1atnTPTfCz0PJQKITW1C1T6TunLwdOHmDgLQN5tv6zNl+gCXkbNsD778OH\nH8L+/XDFFW7mje7d3WzzJnhZaHkomENr79G99Jvbj2k/TqNGiRpEt4+mWvFqXpdlTLo6exbmzoVJ\nk9x9XrGx7r6v7t2hSxe4zNYiDToWWh4KxtBSVSatm8SjXz3KybMnGdxwMI/VfYwc2TLHPWXGJOfP\nP93s8pMmwbp1kCuXu/ere3do0QIyyW2VIc9Cy0PBFlo7Du0gcmYk87fPp16ZekxoP4HrLrvO67KM\nyXDr1rnw+ugjN0Fv8eJuyqju3W11Za9ZaHkoWEIrLj6OUatG8fTCpxERXm/6Or1q9rL5Ak2Wd+YM\nzJnjAmz2bNd9eNNN7vpXhw5w1VVeV5j1WGh5KBhC68c/fyQ8Jpzlu5bT8pqWjG07ljKFy3hakzHB\naP9+1/KaNMkN5ACoWtWFV4cOUL062MxlgWeh5SEvQ+ts3FneWPoGLy5+kQK5CjCi5Qi6Vu5q8wUa\n44etWyEmBmbMgGXL3ES9pUq5a2AdOkDDhu6amEl/Floe8iq01uxZQ4+YHmzYt4FOFTvxTqt3uDz/\n5RlehzGZwZ9/wqxZLsTmzYOTJ6FgQWjVygVY69ZQpIjXVWYeFloeyujQOnn2JIO/HcywZcO4PP/l\njG4zmo43dMyw9zcmszt5EhYscC2wmTNdl2KOHG4KqQ4dXEvMroOljYWWhzIytBb/bzE9Y3ryy8Ff\nCK8ezrDmwyiSx/78MyZQ4uPdzBszZrjHTz+57VWrutZX/fpQty4UKuRtnaHGQstDGRFaR04f4ckF\nTzJm9RjKFSnH+HbjaVK+SUDf0xjzbwmvgy1fDnFxbuLeatWgXr3zj8utp/6CLLQ8FOjQmvPLHHrN\n6sWuI7t4uM7DvNToJfLnCpn/1sZkWseOwYoVsGQJLF7svj51yu27/vrzAVa/vutOtPFR51loeShQ\nofXXib94ZN4jfLjhQ8KKhRHdPpo6peqk+/sYY9LHmTOwZs35EFu6FA4dcvtKlTofYPXqQYUKWXtp\nFQstD6V3aKkqn27+lAfnPsjfp/7m6Vuf5ul6T5M7R+50ew9jTODFx8OmTedDbMkS2LvX7bv0UqhT\nx03se+5xww2QN6+3NWcUCy0PpWdo7Tm6h96zexPzcww1r6zJxPYTqXxF5XR5bWOMt1TdopbnQmzN\nGjew48wZtz9bNrj66n8GWcWKcN11mW/JFQuthC8u0hIYAWQHJqjqa4n2i29/a+AE0F1V1/pzblLS\nI7RUlegfonn8q8c5HXeaIY2G0L9Of5vg1phMLjYWtm1zLbJzj82b4Zdf3CAPcIF1/fXnQ+xcoJUr\nB9mze1v/xfIntNLyuzzd6w1UaIlIdmAr0AzYBawCuqjqlgTHtAYexH2jtYERqlrbn3OTktbQ2v73\ndiJmRvD1b1/T4KoGTGg/gWsuveaiX88YE/pOnYKffz4fYucC7bffzh+TI4ebBPjKK92jZMnzXyd8\nXqRI8A0CSSm00vK7PBD1BrL5UAvYpqrbAUTkY6ADkDB4OgAfqEvOFSJSRERKAGX9ODfdxMXHMXLl\nSJ75+hlyZMvB2LZj6Vmjp01wa4whTx53L1jVqv/cfuwY/PijC7Bt22DPHvfYts11OR48mPRrJRVq\nxYq5a2h58rh/E3+d8Hnu3BkefBf9u1xV96Z3MYEMrZLAzgTPd+ESOKVjSvp5brr4++TftPqoFSt3\nr6TNtW2IahtFqUKlAvFWxphMpEABN0P9TTclvf/kSTfYY88e2L37fKide752rZvl48SJ1L934kAr\nUcJdnwuQtPwuD6nQyhAiEglEAuS6iBk1i+QpwtWXXs1DtR+iS6UuNsGtMSZd5M0L5cu7R3JU4ehR\nt8bYqVMu6M49UvM8X740lZpDRFYneD5OVcel6RUDKJChtRsoneB5Kd82f47J6ce5APh+uOPAXdNK\nbZEiwke3f5Ta04wxJs1E3LRTHk89FauqNS+wPy2/y9NdIC/arAKuFZFyIpIL6AzEJDomBrhPnDrA\nYV8fqD/nGmOMCby0/C5PdwFraalqrIj0A+bhhklOVNXNItLLtz8KmIMbbbINN0zy/gudG6hajTHG\nJC0tv8sDwW4uNsaYLCzUbi62Md3GGGNChoWWMcaYkGGhZYwxJmRYaBljjAkZFlrGGGNCRqYaPSgi\n8cDJizw9BxCbjuWkN6svbay+tLH60iaY68urqiHTgMlUoZUWIrI6hbvCPWX1pY3VlzZWX9oEe32h\nJGTS1RhjjLHQMsYYEzIstM4L2lmNfay+tLH60sbqS5tgry9k2DUtY4wxIcNaWsYYY0JGpg8tEWkp\nIj+LyDYReTKJ/SIiI337N4hIDX/PzaD6uvrq2igiy0SkaoJ9O3zb1yVaxC0j62soIod9NawTkef9\nPTeD6huQoLZNIhInIpf69mXEz2+iiOwXkU3J7Pf685dSfV5//lKqz+vPX0r1efr5y5RUNdM+cNPo\n/wqUB3IB64GwRMe0BuYCAtQBVvp7bgbVVxe4xPd1q3P1+Z7vAIp6/PNrCMy6mHMzor5Ex7cDvs6o\nn5/vPeoDNYBNyez37PPnZ32eff78rM+zz58/9Xn9+cuMj8ze0qoFbFPV7ap6BvgY6JDomA7AB+qs\nAIqISAk/zw14faq6TFX/9j1dgVsRNKOk5WcQFD+/RLoAU9K5hgtS1cXAwQsc4uXnL8X6PP78+fPz\nS05Q/PwSyfDPX2aU2UOrJLAzwfNdvm3+HOPPuRlRX0LhuL/Kz1FggYisEZHIdK4tNfXV9XUhzRWR\niqk8NyPqQ0TyAS2BqQk2B/rn5w8vP3+pldGfP3959fnzWxB//kJOwFYuNulLRBrhfmncmmDzraq6\nW0QuB+aLyE++v/wy0lqgjKoeE5HWwBfAtRlcgz/aAUtVNeFfxcHw8wsJ9vlLM/v8pZPM3tLaDZRO\n8LyUb5s/x/hzbkbUh4hUASYAHVT1wLntqrrb9+9+YDquSyRD61PVI6p6zPf1HCCniBT159yMqC+B\nziTqmsmAn58/vPz8+cXDz1+KPP78pUawfv5Cj9cX1QL5wLUktwPlOH8xtmKiY9rwzwvh3/t7bgbV\nVwbYBtRNtD0/UDDB18uAlh7UV5zz9/vVAn73/SyD4ufnO64w7rpD/oz8+SV4r7IkP5DAs8+fn/V5\n9vnzsz7PPn/+1BcMn7/M9sjU3YOqGisi/YB5uNFEE1V1s4j08u2PAubgRnBtA04A91/oXA/qex64\nDBgtIgCx6ibevAKY7tuWA5isql96UN+dQG8RicXNsN9Z3f+JwfLzA7gN+EpVjyc4PeA/PwARmYIb\n4VZURHYBg4CcCerz7PPnZ32eff78rM+zz5+f9YGHn7/MyGbEMMYYEzIy+zUtY4wxmYiFljHGmJBh\noWWMMSZkWGgZY4wJGRZaxhhjQoaFljF+EJHSIvJbghm6L/E9L5vCeS+IyOMpHNNRRMLSr1pjMi8L\nLWP8oKo7gTHAa75NrwHjVHVHOrx8R8BCyxg/2H1axvhJRHICa4CJQARQTVXPJnHcM0A3YD9u0tY1\nqjpMRCKASNwMDduAe4FqwCzgsO9xB9A48XGqeiKw350xocFCy5hUEJEWwJdAc1Wdn8T+G4FJQG3c\nTAdrgShfaF2mvrn7RGQIsE9V3xGRSbg1oT737UvyuMB/d8YEP+seNCZ1WgF7gUrJ7K8HTFfVE6p6\nBIhJsK+SiCwRkY1AV6Bikq/g/3HGZDkWWsb4SUSqAc1wE9s+4lusMTUmAf1UtTIwGMiTxuOMyXIs\ntIzxg7iZTccAD6vq78BQYFgShy4GOopIXhEpiFtH6ZyCwF7ftbGuCbYf9e1L6ThjsjwLLWP8EwH8\nnuA61miggog0SHiQqq4FPsEthTEXWJVg93PASmAp8FOC7R8DA0TkBxG5+gLHGZPl2UAMY4wxIcNa\nWsYYY0KGhZYxxpiQYaFljDEmZFhoGWOMCRkWWsYYY0KGhZYxxpiQYaFljDEmZFhoGWOMCRn/B2ij\ncs4twFCIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f597ea76a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax1.plot(x, y1, 'g-')   # green, solid line\n",
    "ax1.set_ylabel('Y1 data', color='g')\n",
    "\n",
    "ax2.plot(x[::-1], y2, 'b-') # blue\n",
    "ax2.set_ylabel('Y2 data', color='b')\n",
    "\n",
    "ax1.set_xlabel('X data')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
